{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "np.random.seed(1218)\n",
    "x = np.random.uniform(-3,3,size=100)\n",
    "X = x.reshape(-1,1)\n",
    "y = 0.5 * x**2 + x + 2 + np.random.normal(0,1,100) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75, 1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression \n",
    "from sklearn.metrics import mean_squared_error\n",
    "train_score = []\n",
    "test_score = []\n",
    "\n",
    "for i in range(1,76):\n",
    "    lin_reg = LinearRegression()\n",
    "    lin_reg.fit(X_train[:i],y_train[:i])\n",
    "    y_train_predict = lin_reg.predict(X_train[:i])\n",
    "    train_score.append(mean_squared_error(y_train[:i],y_train_predict))\n",
    "    y_test_predict = lin_reg.predict(X_test)\n",
    "    test_score.append(mean_squared_error(y_test,y_test_predict))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112810160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([i for i in range(1,76)],np.sqrt(train_score), label = 'train')\n",
    "plt.plot([i for i in range(1,76)],np.sqrt(test_score), label = 'test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
